Nobody Wants ML, AI, and Analytics.
They want a UX that provides indispensable decision support, useful insights, and actionable intelligence.
My free resources below will help you learn how to use human-centered design to create innovative user experiences that turn technical outputs into valuable customer outcomes.
Are customers not getting the value out of your data product, analytics SAAS, or decision support application?
My free self-assessment guide covers 9 key topics to help you make your service indispensable. Each day, for 9 days, you will also get an email lesson that goes deeper into the topic and provides recommendations on how to start taking action.
Want to learn how to design engaging data products your customers and stakeholders will use and value?
My self-guided video course—Designing Human-Centered Data Products—can help you learn the creative problem solving skills that data-driven software leaders need to produce useful, usable applications and solutions. Download the first module's video and written supplement, free.
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Recent Articles by Brian
Today, I’m sharing my impressions of one of Spotify’s analytics touchpoints—a monthly email I receive with a boatload of design choices I mostly hope you will not copy, especially if you’re working in an enterprise capacity. Most of you by now probably know … Read more
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Presenting data and evidence isn’t the same thing as providing indispensable decision support, especially when your insights are experienced in a software application with no Powerpoint deck, narrator, or intimate storytelling.
Customers want simple, well-designed decision support tools and UX’s that are actionable. Businesses want to see value from data and adoption of data-driven decision making. However, the UX that is afforded to is often simply a byproduct of the analytics team’s engineering, or, at best, “data viz” efforts—and it’s not working. A decade later, success rates for data projects remain unchanged, despite vendor/BI tooling improvements. What are BI/analytics teams still missing? Design.
In many cases, machine learning needs to be deployed to augment human decision making, not automate it. What are you doing to account for this dependency on the success of your data product?
Self-reflecting on #BLM, the makeup of my podcast guests to date, racism, and the responsibilities of consultants with platforms and audiences in fighting injustice.
I performed a rapid UI/UX and data visualization audit on the MITRE Covid-19 Healthcare Coalition Decision Support Dashboard. Watch it here, and see my recommended design changes the team should make.
How to avoid spending 6-8 months on a technically right, effectively wrong model. Are you successful with ML if nobody uses your solution, model, or application?